{"type":"ScholarlyArticle","title":"Multi-Graph Fusion Networks for Urban Region Embedding","authors":[{"name":"Shangbin Wu","slug":"shangbin-wu","given":"Shangbin","family":"Wu","affiliation":"Fujian Key Laboratory of Sensing and Computing for Smart Cities , School of Informatics , Department of Computer Science and Technology , Xiamen University , China"},{"name":"Xu Yan","slug":"xu-yan","given":"Xu","family":"Yan","affiliation":"Fujian Key Laboratory of Sensing and Computing for Smart Cities , School of Informatics , Department of Computer Science and Technology , Xiamen University , China"},{"name":"Xiaoliang Fan","slug":"xiaoliang-fan","given":"Xiaoliang","family":"Fan","affiliation":"Fujian Key Laboratory of Sensing and Computing for Smart Cities , School of Informatics , Department of Computer Science and Technology , Xiamen University , China"},{"name":"Shirui Pan","slug":"shirui-pan","given":"Shirui","family":"Pan","affiliation":"Department of Data Science and AI , Faculty of Information Technology , Monash University , Australia"},{"name":"Shichao Zhu","slug":"shichao-zhu","given":"Shichao","family":"Zhu","affiliation":"Institute of Information Engineering , Chinese Academy of Sciences , China; School of Cyber Security , University of Chinese Academy of Sciences , China"},{"name":"Chuanpan Zheng","slug":"chuanpan-zheng","given":"Chuanpan","family":"Zheng","affiliation":"Fujian Key Laboratory of Sensing and Computing for Smart Cities , School of Informatics , Department of Computer Science and Technology , Xiamen University , China"},{"name":"Ming Cheng","slug":"ming-cheng","given":"Ming","family":"Cheng","affiliation":"Fujian Key Laboratory of Sensing and Computing for Smart Cities , School of Informatics , Department of Computer Science and Technology , Xiamen University , China"},{"name":"Cheng Wang","slug":"cheng-wang","given":"Cheng","family":"Wang","affiliation":"Fujian Key Laboratory of Sensing and Computing for Smart Cities , School of Informatics , Department of Computer Science and Technology , Xiamen University , China"}],"abstract":"Learning the embeddings for urban regions from human mobility data can reveal the functionality of regions, and then enables the correlated but distinct tasks such as crime prediction. Human mobility data contains rich but abundant information, which yields to the comprehensive region embeddings for cross domain tasks. In this paper, we propose multi-graph fusion networks (MGFN) to enable the cross domain prediction tasks. First, we integrate the graphs with spatio-temporal similarity as mobility patterns through a mobility graph fusion module. Then, in the mobility pattern joint learning module, we design the multi-level cross-attention mechanism to learn the comprehensive embeddings from multiple mobility patterns based on intra-pattern and inter-pattern messages. Finally, we conduct extensive experiments on real-world urban datasets. Experimental results demonstrate that the proposed MGFN outperforms the state-of-the-art methods by up to 12.35% improvement. https://github.com/wushangbin/MGFN","doi":"10.24963/ijcai.2022/321","url":"https://ijcai.org/proceedings/2022/321","pdf":"https://ijcai.org/proceedings/2022/0321.pdf","markdown":"https://ijcai.org/proceedings/2022/321.md","bibtex":"https://ijcai.org/proceedings/2022/bibtex/321","volume":{"slug":"2022","title":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2022","url":"https://ijcai.org/proceedings/2022/","year":2022,"location":"Vienna","dates":"23-29 July 2022","editors":"Luc De Raedt","isbn":"978-1-956792-00-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2022"},"section":"Data Mining","track":"Main Track","number":321,"pages":{"first":2312,"last":2318},"keywords":["Data Mining: Mining Spatial and/or Temporal Data","Data Mining: Mining Graphs","Data Mining: Mining Heterogenous Data"],"videos":[{"label":"Video #1","url":"https://slideslive.com/38986518","length_sec":90,"provider":"slideslive"},{"label":"Video #2","url":"https://slideslive.com/38984661","length_sec":351,"provider":"slideslive"}],"references":[{"position":1,"raw":"[Belkin and Niyogi, 2001] Mikhail Belkin and Partha Niyogi. 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